Dforest: Decision Forest

Provides R-implementation of Decision forest algorithm, which combines the predictions of multiple independent decision tree models for a consensus decision. In particular, Decision Forest is a novel pattern-recognition method which can be used to analyze: (1) DNA microarray data; (2) Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) data; and (3) Structure-Activity Relation (SAR) data. In this package, three fundamental functions are provided, as (1)DF_train, (2)DF_pred, and (3)DF_CV. run Dforest() to see more instructions. Weida Tong (2003) <doi:10.1021/ci020058s>.

Version:	0.4.2
Depends:	R (≥ 3.0)
Imports:	rpart, ggplot2, methods, stats
Published:	2017-11-28
DOI:	10.32614/CRAN.package.Dforest
Author:	Leihong Wu, Weida Tong (Weida.tong@fda.hhs.gov)
Maintainer:	Leihong Wu <leihong.wu at fda.hhs.gov>
License:	GPL-2
NeedsCompilation:	no
CRAN checks:	Dforest results

Documentation:

Reference manual:

Dforest.pdf

Downloads:

Package source:	Dforest_0.4.2.tar.gz
Windows binaries:	r-devel: Dforest_0.4.2.zip, r-release: Dforest_0.4.2.zip, r-oldrel: Dforest_0.4.2.zip
macOS binaries:	r-release (arm64): Dforest_0.4.2.tgz, r-oldrel (arm64): Dforest_0.4.2.tgz, r-release (x86_64): Dforest_0.4.2.tgz, r-oldrel (x86_64): Dforest_0.4.2.tgz
Old sources:	Dforest archive

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